Department of Cell & Molecular Biology, Faculty of Life Sciences & Biotechnology, Shahid Beheshti University, Tehran, 19839-69411, Iran.
Department of Plant Agriculture, University of Guelph, Guelph, ON, Canada.
BMC Biotechnol. 2024 Sep 27;24(1):68. doi: 10.1186/s12896-024-00898-7.
Developing somatic embryogenesis is one of the main steps in successful in vitro propagation and gene transformation in the carrot. However, somatic embryogenesis is influenced by different intrinsic (genetics, genotype, and explant) and extrinsic (e.g., plant growth regulators (PGRs), medium composition, and gelling agent) factors which cause challenges in developing the somatic embryogenesis protocol. Therefore, optimizing somatic embryogenesis is a tedious, time-consuming, and costly process. Novel data mining approaches through a hybrid of artificial neural networks (ANNs) and optimization algorithms can facilitate modeling and optimizing in vitro culture processes and thereby reduce large experimental treatments and combinations. Carrot is a model plant in genetic engineering works and recombinant drugs, and therefore it is an important plant in research works. Also, in this research, for the first time, embryogenesis in carrot (Daucus carota L.) using Genetic algorithm (GA) and data mining technology has been reviewed and analyzed.
In the current study, data mining approach through multilayer perceptron (MLP) and radial basis function (RBF) as two well-known ANNs were employed to model and predict embryogenic callus production in carrot based on eight input variables including carrot cultivars, agar, magnesium sulfate (MgSO), calcium dichloride (CaCl), manganese (II) sulfate (MnSO), 2,4-dichlorophenoxyacetic acid (2,4-D), 6-benzylaminopurine (BAP), and kinetin (KIN). To confirm the reliability and accuracy of the developed model, the result obtained from RBF-GA model were tested in the laboratory.
The results showed that RBF had better prediction efficiency than MLP. Then, the developed model was linked to a genetic algorithm (GA) to optimize the system. To confirm the reliability and accuracy of the developed model, the result of RBF-GA was experimentally tested in the lab as a validation experiment. The result showed that there was no significant difference between the predicted optimized result and the experimental result.
Generally, the results of this study suggest that data mining through RBF-GA can be considered as a robust approach, besides experimental methods, to model and optimize in vitro culture systems. According to the RBF-GA result, the highest somatic embryogenesis rate (62.5%) can be obtained from Nantes improved cultivar cultured on medium containing 195.23 mg/l MgSO, 330.07 mg/l CaCl, 18.3 mg/l MnSO, 0.46 mg/l 2,4- D, 0.03 mg/l BAP, and 0.88 mg/l KIN. These results were also confirmed in the laboratory.
体细胞胚胎发生的发育是胡萝卜成功进行体外繁殖和基因转化的主要步骤之一。然而，体细胞胚胎发生受到不同内在（遗传、基因型和外植体）和外在（例如植物生长调节剂（PGRs）、培养基组成和凝胶剂）因素的影响，这给体细胞胚胎发生方案的发展带来了挑战。因此，优化体细胞胚胎发生是一个繁琐、耗时且昂贵的过程。通过人工神经网络（ANNs）和优化算法的混合进行新的数据挖掘方法可以促进体外培养过程的建模和优化，从而减少大量的实验处理和组合。胡萝卜是遗传工程工作和重组药物的模式植物，因此它是研究工作中的重要植物。此外，在这项研究中，首次使用遗传算法（GA）和数据挖掘技术综述和分析了胡萝卜（Daucus carota L.）的胚胎发生。
在目前的研究中，使用多层感知器（MLP）和径向基函数（RBF）作为两种著名的人工神经网络，通过数据挖掘方法对基于包括胡萝卜品种、琼脂、硫酸镁（MgSO）、氯化钙（CaCl）、硫酸锰（MnSO）、2,4-二氯苯氧乙酸（2,4-D）、6-苄基氨基嘌呤（BAP）和激动素（KIN）在内的八个输入变量的胡萝卜体细胞胚发生的产生进行建模和预测。为了确认所开发模型的可靠性和准确性，将从 RBF-GA 模型获得的结果在实验室中进行测试。
结果表明，RBF 比 MLP 具有更好的预测效率。然后，将开发的模型与遗传算法（GA）链接以优化系统。为了确认所开发模型的可靠性和准确性，作为验证实验，在实验室中对 RBF-GA 的结果进行了实验测试。结果表明，预测优化结果与实验结果之间没有显著差异。
总的来说，这项研究的结果表明，通过 RBF-GA 进行的数据挖掘除了实验方法之外，还可以被认为是一种用于建模和优化体外培养系统的稳健方法。根据 RBF-GA 的结果，在含有 195.23mg/l MgSO、330.07mg/l CaCl、18.3mg/l MnSO、0.46mg/l 2,4-D、0.03mg/l BAP 和 0.88mg/l KIN 的培养基中培养的 Nantes 改良品种可获得最高的体细胞胚胎发生率（62.5%）。这些结果在实验室中也得到了证实。
